0특-04문정언(f5.16)(189~207)

Size: px

Start display at page:

Download "0특-04문정언(f5.16)(189~207)"

사랑 종
7 years ago
Views:

1 Korean Journal of Remote Sensing, Vol.26, No.2, 2010, pp.189~207 Development of Ocean Environmental Algorithms for Geostationary Ocean Color Imager (GOCI) Jeong-Eon Moon*, **, Yu-Hwan Ahn*, Joo-Hyung Ryu*, and Palanisamy Shanmugam*** *Korea Ocean Satellite Center, KORDI, **Department of Oceanography, Inha University ***Department of Ocean Engineering, IIT Madras, India Abstract : Several ocean color algorithms have been developed for GOCI (Geostationary Ocean Color Imager) using in-situ bio-optical data sets. These data sets collected around the Korean Peninsula between 1998 and 2009 include chlorophyll-a concentration (Chl-a), suspended sediment concentration (SS), absorption coefficient of dissolved organic matter (a dom ), and remote sensing reflectance (R rs ) obtained from 1348 points. The GOCI Chl-a algorithm was developed using a 4-band remote sensing reflectance ratio that account for the influence of suspended sediment and dissolved organic matter. The GOCI Chl-a algorithm reproduced in-situ chlorophyll concentration better than the other algorithms. In the SeaWiFS images, this algorithm reduced an average error of 46 % in chlorophyll concentration retrieved by standard chlorophyll algorithms of SeaWiFS. For the GOCI SS algorithm, a single band was used (Ahn et al., 2001) instead of a band ratio that is commonly used in chlorophyll algorithms. The GOCI a dom algorithm was derived from the relationship between remote sensing reflectance band ratio (R rs (412)/R rs (555)) and a dom (l). The GOCI Chl-a fluorescence and GOCI red tide algorithms were developed by Ahn and Shanmugam (2007) and Ahn and Shanmugam (2006), respectively. If the launch of GOCI in June 2010 is successful, then the developed algorithms will be analyzed in the GOCI CAL/VAL processes, and improved by incorporating more data sets of the ocean optical properties data that will be obtained from waters around the Korean Peninsula. Key Words : GOCI, Ocean Color, Chlorophyll, SS, DOM. a dom R rs yhahn@kordi.re.kr 189

2 Korean Journal of Remote Sensing, Vol.26, No.2, 2010 a dom R rs R rs a dom l 190

3 Development of Ocean Environmental Algorithms for Geostationary Ocean Color Imager (GOCI) dinoflagellate Ptychodiscus brevis Karenia brevis 191

4 Korean Journal of Remote Sensing, Vol.26, No.2, 2010 a dom Chl-a (mg/m 3 ) = R _ (1) Fig. 1. The observation map. 192

5 Development of Ocean Environmental Algorithms for Geostationary Ocean Color Imager (GOCI) Table 1. The observation schedule during no. Sampling Platform Time Period Sampling Location Number of Stations Visited 193

6 Korean Journal of Remote Sensing, Vol.26, No.2, 2010 Fig. 2. The relationships between in-situ chlorophyll-a concentration (Chl-a) and 2-bands remote sensing reflectance (R rs ) ratios. (a) Chl-a and R rs (443)/R rs (555), (b) Chl-a and R rs (490)/R rs (555). Fig. 3. The relationship between in-situ chlorophyll-a concentration (Chl-a) and 4-bands remote sensing reflectance ratio. {R rs (443) + R rs (490)} _ R rs (412) where, R = R rs (555) Table 2. The comparisons for chlorophyll-a algorithms Name Equation Reference RMSE OC2v2 10 (0.2974_ R R 2_ R 3 ) _ R = log 10( R rs 490 R rs 555 ) O Reilly et al. (1998) 0.28 OC4v4 (SeaWiFS standard) YOC Chl-a GOCI Chl-a 10 (0.366_ 3.067R+1.930R R 3_ 1.532R 4 ) R = log 10( Max(R rs 443, R rs 490, R rs 510) R rs 555 ) 10 ( _ R R 2 ) R = log 10[( R rs 443 R )( rs 412 R rs 555 R rs R _ ) _ 0.8 R = (R rs443 + R rs 490) _ R rs 412 R rs 555 ] O Reilly et al. (1998) 0.30 Siswanto et al. (2010) 0.23 In this Study

7 Development of Ocean Environmental Algorithms for Geostationary Ocean Color Imager (GOCI) Fig. 4. The relationships between in-situ chlorophyll-a concentration (Chl-a) and Chl-a obtained from algorithms. (a) GOCI Chl-a algorithm, (b) YOC Chl-a algorithm, (c) OC4v4 algorithm, (d) OC2v2 algorithm. Fig. 5. The comparison for chlorophyll distribution images obtained from GOCI Chl-a, YOC Chl-a, OC4v4 and OC2v2 algorithms using SeaWiFS data in March 19, Table 3. The comparisons for chlorophyll-a concentration obtained from red box area of Fig. 5 GOCI Chl-a YOC Chl-a OC4v4 OC2v2 Average 1.18 mg/m mg/m mg/m mg/m 3 vs. OC4v4 53 % 48 % decrease decrease 40 % 35 % vs. OC2v2 decrease decrease Selection Area: Lat. 33 _ 34, Long. 124 _ 125 (Red Box of Fig. 5)

8 Korean Journal of Remote Sensing, Vol.26, No.2, 2010 Fig. 7. The relationship between in-situ suspended sediment concentration (SS) and remote sensing reflectance at 555 nm (R rs (555)). Fig. 6. The relationships between in-situ suspended sediment concentration (SS) and 2-bands remote sensing reflectance (R rs ) ratios. (a) SS and R rs (412)/R rs (555), (b) SS and R rs (443)/R rs (555), (c) SS and R rs (490)/R rs (555). 196

9 Development of Ocean Environmental Algorithms for Geostationary Ocean Color Imager (GOCI) SS(g/m 3 ) = (R rs (555)) (2) Table 4. The comparisons for suspended sediment algorithms Name Equation Reference RMSE SS = _ R R 2_ R R 4_ R Clark 5 TSM R = log 10( nl w (412) + nl w (443) MODIS ATBD (1997) 0.61 (include SeaDAS) nl w (510) ) YOC TSM SS = R1_ R2 R1 = R rs (555) + R rs (670) R rs (490) R2 = R rs(555) Siswanto et al. (2010) 0.33 GOCI SS SS = R R = R rs (555) In this Study 0.28 Fig. 8. The relationships between in-situ suspended sediment concentration (SS) and SS obtained from algorithms. (a) Clark TSM algorithm, (b) YOC TSM algorithm, (c) GOCI SS algorithm. 197

10 Korean Journal of Remote Sensing, Vol.26, No.2, 2010 a dom a dom a dom a dom a dom a dom a dom a dom a dom a dom (400)[m _1 ] = R _ (3) a dom (412)[m _1 ] = R _ (4) Fig. 9. The relationships between in-situ absorption coefficients of dissolved organic matter (a dom ) with wavelengths and 2-bands remote sensing reflectance (R rs ) ratios. (a) a dom (400) and R rs (412)/Rrs(555), (b) a dom (400) and R rs (443)/R rs (555), (c) a dom (400) and R rs (490)/R rs (555), (d) a dom (412) and R rs (412)/R rs (555), (e) a dom (412) and R rs (443)/R rs (555), (f) a dom (412) and R rs (490)/R rs (555). 198

11 Development of Ocean Environmental Algorithms for Geostationary Ocean Color Imager (GOCI) R rs (412) where, R = R rs (555) 1 a dom (l) S = ( ) ln ( ) (5) l _ l 0 a dom (l 0 ) 1 a dom (412) S = ( ) ln ( ) (6) 12 a dom (400) a dom l a dom a dom Table 5. The comparisons for absorption coefficient of dissolved organic matter algorithms Name Equation Reference RMSE a dom (400) = R _ GOCI a R rs (412) dom (400) In this Study 0.18 R = R rs(555) GOCI a dom (412) YOC a dom (440) a dom (412) = R _ R rs (412) R = R rs(555) a dom (440) = 10 _ _ R R 2 R = log 10[( R rs (490) R )(R rs(443)) ] 0.1 rs(555) In this Study 0.18 Siswanto et al. (2010) 0.24 Fig. 10. The relationships between in-situ absorption coefficients of dissolved organic matter (a dom ) and a dom obtained from algorithms. (a) developed GOCI a dom (400) algorithm in this study, (b) developed GOCI a dom (412) algorithm in this study, (c) YOC a dom (440) algorithm. 199

12 Korean Journal of Remote Sensing, Vol.26, No.2, 2010 a dom Fig. 11. Schematic representation of the fluorescence line height (DFlu) estimation using the remote sensing reflectance spectrum (from Ahn and Shanmugam, 2007). D D DFlu = XY = CX _ YC (7) DFlu = XY = CX _ [( R L (l _ (F) l (S) )) _ R S l _ (L) l (S) + R S] (8) l l l D 730 DFlu (Area) = DFlu(l)dl (9) 600 D Fig. 12. Relationships between in-situ chlorophyll concentrations and (a) DFlu(681) and (b) DFlu (area). The solid line is the best-fit regression to our bio-optical dataset (N=118) (from Ahn and Shanmugam, 2007). 200

13 Development of Ocean Environmental Algorithms for Geostationary Ocean Color Imager (GOCI) Table 6. The regression equations and squared correlation coefficients obtained for the fluorescence algorithms (from Ahn and Shanmugam, 2007) Algorithms Correlation coefficient (r (r 2 ) ) Chl-a = [DFlu(681)] Chl-a = [DFlu(area)] D D D D D D Fig. 14. The diagram for relationship among redtide index, chlorophyll-a concentration and suspended solid particle concentration. Fig. 15. (a) Scatterplot of RI calculated using Eq.(10) versus L w at 443 nm for data N=25. (b) Scatterplot of RI calculated using Eq.(10) versus L w at 443 nm for data N=375. Note that RI progressively increased with the decrease of L w at 443 nm (from Ahn and Shanmugam, 2006). Fig. 13. Comparison between the measured and predicted chlorophyll-a concentrations (from Ahn and Shanmugam, 2007). 201

Korean Journal of Remote Sensing, Vol.26, No.2, 2010 [L w (510)/L w (555) _ L w (443)] RI = (10) [L w (510)/L w (555) + L w (443)] RI (D1) = 10 (_ 2.4394 X 3 +5.2587 X 2_ 4.117 X+0.

14 Korean Journal of Remote Sensing, Vol.26, No.2, 2010 [L w (510)/L w (555) _ L w (443)] RI = (10) [L w (510)/L w (555) + L w (443)] RI (D1) = 10 (_ X X 2_ X ) (11) RI (D1) = 10 (_ X X 2_ X+0.919) (12) Fig. 16. Comparison between OC4-Chl-a (a) and RI (b) from SeaWiFS image of 19 September 2000 in the Korea South Sea. The (c) image is red tide information obtained from NFRDI in same date (from Ahn and Shanmugam, 2006). 202

15 Development of Ocean Environmental Algorithms for Geostationary Ocean Color Imager (GOCI) a dom l a dom a dom a dom a dom a dom a dom 203

16 Korean Journal of Remote Sensing, Vol.26, No.2, 2010 D D Ahn, Y. H., J. E. Moon, and S. Gallegos, Development of suspended particulate matter algorithms for ocean color remote sensing. Korean Journal of Remote Sensing, 17(4): Ahn, Y. H., P. Shanmugam, and S. Gallegos, Evolution of suspended sediment patterns in the East China and Yellow Seas. Journal of the Korean Society of Oceanography, 39(1): Ahn, Y. H., P. Shanmugam, K. I. Chang, J. E. Moon, and J. H. Ryu, Spatial and temporal aspects of phytoplankton blooms in complex ecosystems off the Korean coast from satellite ocean color observations. Ocean Science Journal, 40(2): Ahn, Y. H. and P. Shanmugam, Detecting the red tide algal blooms from satellite ocean color observations in optically complex Northeast-Asia Coastal waters. Remote Sensing of Environment, 103(4):

17 Development of Ocean Environmental Algorithms for Geostationary Ocean Color Imager (GOCI) Ahn, Y. H., P. Shanmugam, J. H. Ryu, and J.C. Jeong, Satellite detection of harmful algal bloom occurrence in Korean waters. Harmful Algae, 5(2): Ahn, Y. H. and P. Shanmugam, Derivation and analysis of the fluorescence algorithms to estimate phytoplankton pigment concentreations in optically complex coastal waters. Journal of Optics A: Pure and Applied Optics, 9(4): Bricaud, A., A. Morel, and L. Prieur, Absorption by dissolved organic matter in the sea (yellow substance) in the UV and visible domains. Limnology and Oceanography, 26(1): Carder, K. L. and R. G. Steward, A remotesensing reflectance model of a red-tide dinoflagellate off west Florida. Limnology and Oceanography, 30(2): Carder, K. L., R. G. Steward, G. R. Harvey, and P. B. Ortner, Marine humic and fulvic acids: Their effects on remote sensing of ocean chlorophyll. Limnology and Oceanography, 34(1): Carder, K. L., S. K. Hawes, K. A. Baker, R. C. Smith, R. G. Steward, and B.G. Mitchell, Reflectance model for quantifying chlorophyll a in the presence of productivity degradation products. Journal of Geophysical Research, 96(C11): Carder, K. L., F. R. Chen, Z. P. Lee, S. K. Hawes, and D. Kamykowski, Semianalytic Moderate-Resolution Imaging Spectrometer algorithms for chlorophylla and absorption with bio-optical domains based on nitratedepletion temperatures. Journal of Geophysical Research, 104(C3): Carder, K. L., F. R. Chen, J. P. Cannizzaro, J. W. Campbell, and B. G. Mitchell, Performance of the MODIS semi-analytical ocean color algorithm for chlorophyll-a. Advances in Space Research, 33(7): Corsini, G., R. Grasso, and P. Cipollini, Regional bio-optical algorithms for the Alboran Sea from a reflectance model and in situ data. Geophysical Research Letters, 29(15), 1739, /2001GL Darecki, M. and D. Stramski, An evaluation of MODIS and SeaWiFS bio-optical algorithms in the Baltic Sea. Remote Sensing of Environment, 89(3): Doerffer, R. and J. Fischer, Concentrations of chlorophyll, suspended matter, and gelbstoff in case II waters derived from satellite coastal zone color scanner data with inverse modeling methods. Journal of Geophysical Research, 99(C4): Doerffer, R. and H. Schiller, The MERIS Case 2 water algorithm. International Journal of Remote Sensing, 28(3-4): Garver, S. A. and D. Siegel, Inherent optical property inversion of ocean color spectra and its biogeochemical interpretation 1. Time series from the Sargasso Sea. Journal of Geophysical Research, 102(C8): Gohin, F., J. N. Druon, and L. Lampert, A five channel chlorophyll concentration algorithm applied to SeaWiFS data processed by SeaDAS in coastal waters. International Journal of Remote Sensing, 23(8): Gordon, H. R. and A. Morel, Remote assessment of ocean color for interpretation of satellite visible imagery: a review. In: Lecture Notes on Coastal and Estuarine Studies, edited by Barker, R. T., N. K. Mooers, M. J. Bowman and B. Zeitzschel, Springer-Verlag, New York. Hansell, D. A. and C. A. Carlson, Biogeochemistry of Marine Dissolved Organic Matter. Academic Press. Hu, C., F. E. Muller-Karger, C. J. Taylor, K. L. Carder, C. Kelble, E. Johns, and C. A. Heil, 205

18 Korean Journal of Remote Sensing, Vol.26, No.2, Red tide detection and tracing using MODIS Fluorescence data: A regional example in SW Florida coastal waters. Remote Sensing of Environment, 97(3): Huot, Y., C. A. Brown, and J. J. Cullen, New algorithms for MODIS sun-induced chlorophyll fluorescence and a comparison with present data products. Limnology and Oceanography: Methods, 3: Kahru, M. and B. G. Mitchell, Empirical chlorophyll algorithm and preliminary SeaWiFS validation for the California Current. International Journal of Remote Sensing, 20(17): Kirk, J. T. O., Light and Photosynthesis in Aquatic Ecosystems. Cambridge University Press. Lee, Z. P., K. L. Carder, and R. Arnone, Deriving inherent optical properties from water color: A multi-band quasi-analytical algorithm for optically deep waters. Applied Optics, 41(21): Lee, Z. P., K. P. Du, and R. Arnone, A model for the diffuse attenuation coefficient of downwelling irradiance. Journal of Geophysical Research, 110, C02016, doi: /2004jc Letelier, R. M. and M. R. Abbott, An analysis of chlorophyll fluorescence algorithms for the Moderate Resolution Imaging Spectrometer (MODIS). Remote Sensing of Environment, 58(2): Loise, H. and A. Morel, Light scattering and chlorophyll concentration in case 1 waters: A reexamination, Limnology and Oceanography, 43(5): Maritorena, S., D. A. Siegel, and A. R. Peterson, Optimization of a semianalytical ocean color model for global-scale applications. Applied Optics, 41(15): Meroni, M., M. Rossini, L. Guanter, L. Alonso, U. Rascher, R. Colombo, and J. Moreno, Remote sensing of solar-induced chlorophyll fluorescence: Review of methods and applications. Remote Sensing of Environment, 113(10): Miller, R. L., C. E. D. Castillo, and B. A. McKee, Remote Sensing of Coastal Aquatic Environments. Springer. MODIS ATBD Report, Bio-Optical Algorithms- Case 1 Waters. edited by Clark, D.K.. Morel, A. and L. Prieur, Analysis of variations in ocean color. Limnology and Oceanography, 22(4): O Reilly, J. E., S. Maritorena, B. G. Mitchell, D. A. Siegel, K. L. Carder, S. A. Garver, M. Kahru, and C. McClain, Ocean color chlorophyll algorithm for SeaWiFS. Journal of Geophysical Research, 103(C11): Pradhan, Y., A. V. Thomaskutty, A. S. Rajawat, and S. Nayak, Improved regional algorithm to retrieve total suspended particulate matter using IRS-P4 ocean colour monitor data. Journal of Optics A: Pure and Applied Optics, 7(7): Schalles, J. F., Optical remote sensing techniques to estimate phytoplankton chlorophyll a concentration in coastal waters with varying suspended matter and CDOM concentrations. In: Remote Sensing of Aquatic Coastal Ecosystem Processes, edited by Richardson, L.L. and E.F. LeDrew, Springer, pp Schiller, H. and R. Doerffer, Neural network for emulation of an inverse model-operational derivation of Case II water properties from MERIS data. International Journal of Remote Sensing, 20(9): Shanmugam, P., Y. H. Ahn, and P. S. Ram, SeaWiFS sensing of hazardous algal blooms and their underlying mechanisms in shelfslope waters of the Nothwest Pacific during summer. Remote Sensing of Environment, 206

19 Development of Ocean Environmental Algorithms for Geostationary Ocean Color Imager (GOCI) 112(8): Siswanto, E., J. Tang, Y. H. Ahn, J. Ishizaka, S. J. Yoo, S. W. Kim, Y. Kiyomoto, K. Yamada, C. Chiang, and H. Kawamura, Ocean color algorithms to retrieve chlorophyll-a, total suspended matter and colored dissolved organic matter absorption coefficient in the Yellow and East China Seas. (in preparation). Tang, D. L., H. Kawamura, H. Doan-Nhu, and W. Takahashi, Remote sensing oceanography of a harmful algal bloom off the coast of southeastern Vietnam. Journal of Geophysical Research, 109, C03014, doi: /2003jc Tassan, S., The effect of dissolved yellow substance on the quantitative retrieval of chlorophyll and total suspended sediment concentrations from remote measurements of water colour. International Journal of Remote Sensing, 9(4): Tassan, S., Local algorithms using SeaWiFS data for the retrieval of phytoplankton, pigments, suspended sediment, and yellow substance in coastal waters. Applied Optics, 33(12): Zhang, M., J. Tang, Q. Dong, Q. T. Song, and J. Ding, Retrieval of total suspended matter concentration in the Yellow and East China Seas from MODIS imagery. Remote Sensing of Environment, 114(2):

DBPIA-NURIMEDIA

DBPIA-NURIMEDIA Korean Journal of Remote Sensing, Vol.23, No.4, 2007, pp.311~321 Development K d (l) and Visibility Algorithm for Ocean Color Sensor Around the Central Coasts of the Yellow Sea Jee-Eun Min*, **, Joo-Hyung